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The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explore deep decarbonization pathways for the Canadian energy sector that would allow Canada to participate in global mitigation efforts to keep global mean surface temperatures from increasing by more than 2 °C by 2100. Our approach consists in deriving minimum cost solutions for achieving progressive emission reductions up to 2050 using the North American TIMES Energy Model (NATEM), a detailed multi-regional and integrated optimization energy model. With this model, we analyze a baseline and two 60% reduction scenarios of combustion related emissions by 2050 from 1990 levels, with different assumptions regarding projected demands for energy services and availability of technology options for carbon mitigation. The first reduction scenario includes only well-known technologies while the second one considers additional disruptive technologies, which are known but are not fully developed commercially. Results show that three fundamental transformations need to occur simultaneously in order to achieve ambitious GHG emission reduction targets: electrification of end-use sectors, decarbonization of electricity generating supply, and efficiency improvements. In particular, our results show that electricity represents between 52% and 57% of final energy consumption by 2050, electricity generating supply achieves nearly complete decarbonization by 2025 and final energy consumption decreases by 20% relative to the baseline by 2050. [ABSTRACT FROM AUTHOR]

Most process integration techniques and optimization procedures are based on the implicit assumption that the unit operations and equipment are working efficiently, which is not always the case. Equipment performance analysis by means of key performance indicators is a prerequisite step before undertaking any optimization or improvement measure. However, there are no key performance indicators specifically tailored for the Kraft process equipment, and that take into account both the governing operating conditions and the design parameters. In this paper, new key performance indicators (KPIs) have been developed based on an in depth characterization and dimensional analysis of the Kraft process equipment using the Buckingham Pi (Π) theorem. These KPIs were used to identify process inefficiencies, diagnose causes of inefficiencies and propose remedial actions through a systematic approach. Once adopted, the KPIs proposed could serve to monitor systems performances and allow a tighter process control as they incorporate the design characteristics of the unit operations and their influencing operating conditions. [ABSTRACT FROM AUTHOR]